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	Provedor de dados AgEcon (1) Autor Grover, D.K. (1) Singh, Inder Pal (1) Palavra-chave Agricultural and Food Policy (1) Organic farming (1) Organic wheat (1) Punjab (1) Q12 (1) Q16 (1) Wheat cultivation (1) Mais... Tipo do documento Article (1) Ano 2011 (1) País United States (1) Idioma Inglês (1)		Registro completo Provedor de dados:  CIGR Journal País:  China Título:  Simulation of draft force of winged share tillage tool using artificial neural network model Autores:  Akbarnia, Abbas Mohammadi, Asghar Alimardani, Reza Farhani, Foad Data:  2014-12-30 Ano:  2014 Palavras-chave:  Analysis of variance Back propagation Force evaluation Multi layer perceptron Prediction Resumo:  An artificial neural network (ANN) model, with a back propagation learning algorithm, was developed to predict draft requirements of two winged share tillage tools in a loam soil. The input parameters to the 3–7–1 ANN model were; share width, working depth and operating speed. The output from the network was the draft requirement of each tillage tool. The developed model predicted the draft requirements of the winged share tillage tools with a mean relative error of less than 7% and mean square errors of less than 0.05, when compared to measured draft values. This result indicates that the ANN model had successfully learnt from the training data set to enable correct interpolation and could be used as an alternative tool for modeling soil-tool interaction under specific experimental conditions and soil types. Tipo:  Info:eu-repo/semantics/article Idioma:  Inglês Identificador:  http://www.cigrjournal.org/index.php/Ejounral/article/view/3022 Editor:  International Commission of Agricultural and Biosystems Engineering Relação:  http://www.cigrjournal.org/index.php/Ejounral/article/view/3022/1967 http://www.cigrjournal.org/index.php/Ejounral/article/downloadSuppFile/3022/1032 Formato:  application/pdf Fonte:  Agricultural Engineering International: CIGR Journal; Vol 16, No 4 (2014): CIGR Journal; 57-65 1682-1130 Fechar

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